---
title: "Socioeconomic Factors and Homelessness"
output: 
  flexdashboard::flex_dashboard:
    # logo: logo.png
    orientation: columns
    vertical_layout: fill
    social: menu
    source_code: embed
    theme: readable
---

```{r setup, include=T}
library(ggiraph)
library(tidyverse)
library(gfonts)
library(gt)
library(sf)
library(flextable)
library(tigris)
library(cowplot)
library(patchwork)
library(leaflet)

tooltip_css <- "font-size:20px;padding:2px 4px 2px 4px;background:black;color:white;border-radius:2px 2px 2px 2px;"
```

```{r setup-data, include=F}
# travis_tracts <- read_sf('counts_with_census_indicators.shp')

dfs_merged <- read_csv('counts_with_census_indicators.csv')

tract_counts_08172022 <- read_csv('tract_CA_counts_08172022.csv') %>% 
  rename(GEOID = TRACT)

dfs_merged_with_newcounts <- dfs_merged %>% 
  inner_join(tract_counts_08172022)

# cor.test(dfs_merged_with_newcounts$Counts, dfs_merged_with_newcounts$TotalCount)

# t <- dfs_merged %>% 
#   mutate(Tract = as.character(GEOID)) %>% 
#   arrange(desc(Counts))

# using new CA counts
dfs_condensed <- dfs_merged_with_newcounts %>% 
  dplyr::select(GEOID, total_pop, TotalCount, percent_wo_health_insurance, percent_reporting_bad_mental_health, poverty_rate, Percent_Unemployed, gentrifying_dummy, rent_burden, filing_rate_5yr, percent_overcrowded_rentals, percent_hispanic, percent_nhblack, percent_nhwhite, units_per_sqmi, avg_monthly_reports_per_1k, civic_orgs_per1k, social_service_orgs_per1k, rel_orgs_per1k)

df_no_missing <- dfs_condensed %>%
  na.omit()

df_no_missing_nogeoid <- df_no_missing %>%
  dplyr::select(-GEOID)

# fa_test <- factanal(df_no_missing_nogeoid, factors = 4, rotation = 'varimax')
# 
# fa_test$loadings

dfs_scaled <- dfs_condensed %>% 
  dplyr::select(-GEOID) %>% 
  mutate(across(where(is.numeric), scale))

dfs_scaled_index <- dfs_scaled %>% 
  dplyr::select(TotalCount, percent_wo_health_insurance, percent_reporting_bad_mental_health, poverty_rate, Percent_Unemployed, gentrifying_dummy, rent_burden, filing_rate_5yr, percent_overcrowded_rentals, percent_hispanic, percent_nhblack, percent_nhwhite, units_per_sqmi, avg_monthly_reports_per_1k, social_service_orgs_per1k)

dfs_scaled_index$TotalCount <- dfs_scaled_index$TotalCount*.520
dfs_scaled_index$percent_wo_health_insurance <- dfs_scaled_index$percent_wo_health_insurance*.909
dfs_scaled_index$percent_reporting_bad_mental_health <- dfs_scaled_index$percent_reporting_bad_mental_health*.703
# dfs_scaled_index$Percent_Disability <- dfs_scaled_index$Percent_Disability*.329
dfs_scaled_index$poverty_rate <- dfs_scaled_index$poverty_rate*.520
dfs_scaled_index$Percent_Unemployed <- dfs_scaled_index$Percent_Unemployed*.300
dfs_scaled_index$gentrifying_dummy <- dfs_scaled_index$gentrifying_dummy*.367
dfs_scaled_index$rent_burden <- dfs_scaled_index$rent_burden*.273
dfs_scaled_index$filing_rate_5yr <- dfs_scaled_index$filing_rate_5yr*.689
dfs_scaled_index$percent_overcrowded_rentals <- dfs_scaled_index$percent_overcrowded_rentals*.467
dfs_scaled_index$percent_hispanic <- dfs_scaled_index$percent_hispanic*.976
dfs_scaled_index$percent_nhblack <- dfs_scaled_index$percent_nhblack*.352
dfs_scaled_index$percent_nhwhite <- dfs_scaled_index$percent_nhwhite*-.893
# dfs_scaled_index$units_per_sqmi <- dfs_scaled_index$units_per_sqmi*-.117
dfs_scaled_index$avg_monthly_reports_per_1k <- dfs_scaled_index$avg_monthly_reports_per_1k*.210
dfs_scaled_index$social_service_orgs_per1k <- dfs_scaled_index$social_service_orgs_per1k*-.175

dfs_scaled_index <- dfs_scaled_index %>% 
  replace(is.na(.),0) %>%
  mutate(index = rowSums(.))

tracts <- tracts(state = 'TX', county = 'Travis', year = '2019') %>% 
  dplyr::select(GEOID) %>% 
  mutate(GEOID = as.numeric(GEOID))

dfs_scaled_index_withGEOID <- cbind(dfs_scaled_index, dfs_condensed[c("GEOID")])

dfs_scaled_index_withGEOID <- cbind(dfs_scaled_index_withGEOID, dfs_merged[("Name.x")])

tracts_with_index <- tracts %>% 
  inner_join(dfs_scaled_index_withGEOID)

tracts_with_index$NewIndex =cut(tracts_with_index$index, breaks = classInt::classIntervals(tracts_with_index$index, n = 5, style = 'fisher')$brks, labels = FALSE)

tracts_with_index <- tracts_with_index %>% 
  st_transform(crs = 4326) %>%
  # mutate(Vulnerability = as.factor(NewIndex))
  rename(Vulnerability = NewIndex)

tracts_sf_index_orig_data <- tracts_with_index %>% 
  select(GEOID, Name.x, Vulnerability) %>% 
  inner_join(dfs_condensed)

# names(tracts_with_index)

# flextable part -----
# fun_flextable <- function(dat){
#   ft <- flextable(dat, col_keys = c("Name.x", "Vulnerability", "percent_wo_health_insurance"), 
#                   defaults = list(fontname = "Roboto") )
#   ft <- bold(ft, part = "header", bold = TRUE)
#   ft <- color(ft, color = "blue", part = "all")
#   ft <- set_table_properties(ft, layout = "autofit")
#   as.character(htmltools_value(ft, ft.shadow = FALSE))
# }
# temp_dat <- as.data.frame(tracts_sf_index_orig_data)[,c("Name.x", "Vulnerability", "percent_wo_health_insurance")]
# 
# u <- t(temp_dat)
# 
# temp_dat <- split(temp_dat, seq_len(nrow(temp_dat)))
# tracts_sf_index_orig_data$tooltip <- map_chr(temp_dat, fun_flextable)
# # end of flextable part
# 
# test_ft <- flextable(tracts_with_index)
# 
# test_ft

tracts_map <- tracts_sf_index_orig_data %>%
  ggplot() +
  geom_sf(data = tracts_sf_index_orig_data, color="#dddddd", fill="#f2f2f2", lwd=.3) +
  geom_sf_interactive(aes(fill=Vulnerability, tooltip=tooltip, data_id = Name.x), lwd=.3) +
  # scale_fill_gradientn(colours = tinter("#5da5da", steps = 5), labels = function(x) paste0(x,"Cases")) +
  coord_sf(datum=NA)

tracts_sf_index_orig_data <- tracts_sf_index_orig_data %>% 
  mutate(log_counts = log(TotalCount),
         counts_per_capita = TotalCount/total_pop,
         log_counts_per_capita = TotalCount/total_pop,
         Gentrifying = ifelse(gentrifying_dummy == 1, 'Yes', 'No'))

```

Vulnerability Index Map {data-navmenu="Explore"}
===================================== 

Column {data-width=150}
-------------------------------------

### About This Index

The neighborhood vulnerability index is XYZ and uses multiple data sources to assess XYZ. **Add more here.**

Column {data-width=650}
-------------------------------------

### Travis County Neighborhood Vulnerability Index

```{r map,echo=F}
# girafe(ggobj = tracts_map, width_svg = 10, height_svg = 5,
#        options = list(
#          opts_tooltip(css = "padding:5px;background:white;border-radius:2px 2px 2px 2px;"),
#          opts_hover_inv(css = "opacity:0.5;"),
#          opts_hover(css = "stroke-width:2;")
#        ))

pal <- colorFactor(palette = "BuGn", domain = tracts_sf_index_orig_data$Vulnerability)

area_popup <- paste0("<b>Census Tract:</b> ", tracts_sf_index_orig_data$Name.x,
                     "<br><b>Vulnerability:</b> ", tracts_sf_index_orig_data$Vulnerability,
                     "<br><b>Eviction Rate:</b> ", tracts_sf_index_orig_data$filing_rate_5yr,
                    '<br><b>Pct Uninsured:</b> ', tracts_sf_index_orig_data$percent_wo_health_insurance,
                    '<br><b>Rent Burden:</b> ', tracts_sf_index_orig_data$rent_burden) %>%
  lapply(htmltools::HTML)

leaflet(tracts_sf_index_orig_data) %>%
  setView(-97.726871, 30.307905, zoom = 10) %>% 
  addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
  addProviderTiles(providers$Stamen.Toner, group = "Toner") %>%
  addProviderTiles(providers$CartoDB.Positron, group = "CartoDB") %>%
  addPolygons(fillColor = ~pal(Vulnerability),
              fillOpacity = 0.9,
              opacity = 1,
              color = "black",
              weight = 1,
              label = ~area_popup,
              labelOptions = labelOptions(
                style = list("font-weight" = "normal", padding = "3px 8px"),
                textsize = "15px",
                direction = "auto"),
              group = "Vulnerability",
              highlight = highlightOptions(color = "white",
                                           weight = 0.1)) %>%
  addLegend(data = tracts_sf_index_orig_data,
            position = "bottomright",
            pal = pal,
            values = ~Vulnerability,
            title = "Neighborhood Vulnerability",
            group = "Neighborhood Vulnerability") %>%
  # Layers control
  addLayersControl(
    baseGroups = c("Toner Lite", "Toner", "CartoDB"),
    overlayGroups = "Vulnerability",
    options = layersControlOptions(collapsed = FALSE)
  )
```

Rent and Homelessness {data-navmenu="Explore"}
=========================================

Column
-----------------------------------------

### Rent Burden and Overcrowded Rental Units

```{r rent-plots, echo=F}
rent_burden_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>%
ggplot(mapping = aes(x = rent_burden, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Rent Burden",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

overcrowded_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
ggplot(mapping = aes(x = percent_overcrowded_rentals, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Pct in Overcrowded Rental Unit",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

just_vulnerability_map <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
  ggplot(aes(fill = Vulnerability)) +
    geom_sf_interactive(
    aes(
      tooltip = Name.x,
      data_id = GEOID
    ),
    size = 0.2, color = "black"
  ) +
  theme_void() +
  scale_fill_distiller(palette = 'BuGn', direction = 1)

# scale_fill_viridis_c() 

# eviction_map

# rent_burden_homelessness_scatter

# scatterplots <- plot_grid(eviction_homelessness_scatter, rent_burden_homelessness_scatter, ncol = 1, align = "v",
#                           rel_heights = c(1,1),
#   rel_widths = c(1,1))
# 
# scatterplots

# patch_plot <- ((eviction_homelessness_scatter / rent_burden_homelessness_scatter) | eviction_map)  + plot_layout(widths = c(1,1.2))
# 
# layout <- c(
#   area(1, 1),
#   area(2, 2),
#   area(1, 3)
# )
# 
# # plot(layout)
# 
# test_patch <- (eviction_homelessness_scatter + plot_spacer() + rent_burden_homelessness_scatter) / (plot_spacer() + eviction_map + plot_spacer()) + plot_layout(heights = c(.75,1))
# 
# test_patch_v2 <- eviction_homelessness_scatter + eviction_map + rent_burden_homelessness_scatter + plot_layout(design = layout)

patch_rent_plot <- rent_burden_homelessness_scatter + just_vulnerability_map + overcrowded_homelessness_scatter

# test_patch

# patch_plot

# rent_burden_homelessness_scatter

# eviction_homelessness_scatter

# test_patch_v2

# eviction_map

girafe(
  ggobj = patch_rent_plot,
  height_svg = 5,
  width_svg = 12,
  options = list(
    opts_sizing(rescale = TRUE),
    opts_zoom(max = 5),
    opts_tooltip(css = tooltip_css, delay_mouseover = 0, delay_mouseout = 0),
    opts_hover(css = "fill: #E69F00; fill-opacity: 1.0; stroke: #E69F00;")
  )
)
```

Other Housing Indicators and Homelessness {data-navmenu="Explore"}
=========================================

Column
-----------------------------------------

### Evictions and Gentrification

```{r housing-plots, echo=F}
eviction_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
ggplot(mapping = aes(x = filing_rate_5yr, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Eviction Filing Rate",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

gentrifying_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10,
         !is.na(Gentrifying)) %>%
ggplot(mapping = aes(x = as.factor(Gentrifying), y = counts_per_capita)) +
  geom_boxplot(size = .5,
               outlier.shape = NA) +
  geom_jitter_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  labs(x = "Gentrifying",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

just_vulnerability_map <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
  ggplot(aes(fill = Vulnerability)) +
    geom_sf_interactive(
    aes(
      tooltip = Name.x,
      data_id = GEOID
    ),
    size = 0.2, color = "black"
  ) +
  theme_void() +
  scale_fill_distiller(palette = 'BuGn', direction = 1)

# gentrifying_homelessness_scatter

# scale_fill_viridis_c() 

# eviction_map

# rent_burden_homelessness_scatter

# scatterplots <- plot_grid(eviction_homelessness_scatter, rent_burden_homelessness_scatter, ncol = 1, align = "v",
#                           rel_heights = c(1,1),
#   rel_widths = c(1,1))
# 
# scatterplots

# patch_plot <- ((eviction_homelessness_scatter / rent_burden_homelessness_scatter) | eviction_map)  + plot_layout(widths = c(1,1.2))
# 
# layout <- c(
#   area(1, 1),
#   area(2, 2),
#   area(1, 3)
# )
# 
# # plot(layout)
# 
# test_patch <- (eviction_homelessness_scatter + plot_spacer() + rent_burden_homelessness_scatter) / (plot_spacer() + eviction_map + plot_spacer()) + plot_layout(heights = c(.75,1))
# 
# test_patch_v2 <- eviction_homelessness_scatter + eviction_map + rent_burden_homelessness_scatter + plot_layout(design = layout)

patch_hsg_plot <- eviction_homelessness_scatter + just_vulnerability_map + gentrifying_homelessness_scatter

# test_patch

# patch_plot

# rent_burden_homelessness_scatter

# eviction_homelessness_scatter

# test_patch_v2

# eviction_map

girafe(
  ggobj = patch_hsg_plot,
  height_svg = 5,
  width_svg = 12,
  options = list(
    opts_sizing(rescale = TRUE),
    opts_zoom(max = 5),
    opts_tooltip(css = tooltip_css, delay_mouseover = 0, delay_mouseout = 0),
    opts_hover(css = "fill: #E69F00; fill-opacity: 1.0; stroke: #E69F00;")
  )
)
```

Health Indicators and Homelessness {data-navmenu="Explore"}
=========================================

Column
-----------------------------------------

### Health Insurance and Mental Health

```{r health-plots, echo=F}
insurance_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
ggplot(mapping = aes(x = percent_wo_health_insurance, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Pct Without Health Insurance",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

mental_health_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>%
ggplot(mapping = aes(x = percent_reporting_bad_mental_health, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Pct Reporting Bad Mental Health",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

just_vulnerability_map <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
  ggplot(aes(fill = Vulnerability)) +
    geom_sf_interactive(
    aes(
      tooltip = Name.x,
      data_id = GEOID
    ),
    size = 0.2, color = "black"
  ) +
  theme_void() +
  scale_fill_distiller(palette = 'BuGn', direction = 1)

# scale_fill_viridis_c() 

# eviction_map

# rent_burden_homelessness_scatter

# scatterplots <- plot_grid(eviction_homelessness_scatter, rent_burden_homelessness_scatter, ncol = 1, align = "v",
#                           rel_heights = c(1,1),
#   rel_widths = c(1,1))
# 
# scatterplots

# patch_plot <- ((eviction_homelessness_scatter / rent_burden_homelessness_scatter) | eviction_map)  + plot_layout(widths = c(1,1.2))
# 
# layout <- c(
#   area(1, 1),
#   area(2, 2),
#   area(1, 3)
# )
# 
# # plot(layout)
# 
# test_patch <- (eviction_homelessness_scatter + plot_spacer() + rent_burden_homelessness_scatter) / (plot_spacer() + eviction_map + plot_spacer()) + plot_layout(heights = c(.75,1))
# 
# test_patch_v2 <- eviction_homelessness_scatter + eviction_map + rent_burden_homelessness_scatter + plot_layout(design = layout)

patch_health_plots <- insurance_homelessness_scatter + just_vulnerability_map + mental_health_homelessness_scatter

# test_patch

# patch_plot

# rent_burden_homelessness_scatter

# eviction_homelessness_scatter

# test_patch_v2

# eviction_map

girafe(
  ggobj = patch_health_plots,
  height_svg = 5,
  width_svg = 12,
  options = list(
    opts_sizing(rescale = TRUE),
    opts_zoom(max = 5),
    opts_tooltip(css = tooltip_css, delay_mouseover = 0, delay_mouseout = 0),
    opts_hover(css = "fill: #E69F00; fill-opacity: 1.0; stroke: #E69F00;")
  )
)
```

Poverty and Homelessness {data-navmenu="Explore"}
=========================================

Column
-----------------------------------------

### Poverty and Unemployment Rate

```{r poverty-plots, echo=F}
poverty_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
ggplot(mapping = aes(x = poverty_rate, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Poverty Rate",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

unemployment_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>%
ggplot(mapping = aes(x = Percent_Unemployed, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Unemployment Rate",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

just_vulnerability_map <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
  ggplot(aes(fill = Vulnerability)) +
    geom_sf_interactive(
    aes(
      tooltip = Name.x,
      data_id = GEOID
    ),
    size = 0.2, color = "black"
  ) +
  theme_void() +
  scale_fill_distiller(palette = 'BuGn', direction = 1)

# scale_fill_viridis_c() 

# eviction_map

# rent_burden_homelessness_scatter

# scatterplots <- plot_grid(eviction_homelessness_scatter, rent_burden_homelessness_scatter, ncol = 1, align = "v",
#                           rel_heights = c(1,1),
#   rel_widths = c(1,1))
# 
# scatterplots

# patch_plot <- ((eviction_homelessness_scatter / rent_burden_homelessness_scatter) | eviction_map)  + plot_layout(widths = c(1,1.2))
# 
# layout <- c(
#   area(1, 1),
#   area(2, 2),
#   area(1, 3)
# )
# 
# # plot(layout)
# 
# test_patch <- (eviction_homelessness_scatter + plot_spacer() + rent_burden_homelessness_scatter) / (plot_spacer() + eviction_map + plot_spacer()) + plot_layout(heights = c(.75,1))
# 
# test_patch_v2 <- eviction_homelessness_scatter + eviction_map + rent_burden_homelessness_scatter + plot_layout(design = layout)

patch_poverty_plots <- poverty_homelessness_scatter + just_vulnerability_map + unemployment_homelessness_scatter

# test_patch

# patch_plot

# rent_burden_homelessness_scatter

# eviction_homelessness_scatter

# test_patch_v2

# eviction_map

girafe(
  ggobj = patch_poverty_plots,
  height_svg = 5,
  width_svg = 12,
  options = list(
    opts_sizing(rescale = TRUE),
    opts_zoom(max = 5),
    opts_tooltip(css = tooltip_css, delay_mouseover = 0, delay_mouseout = 0),
    opts_hover(css = "fill: #E69F00; fill-opacity: 1.0; stroke: #E69F00;")
  )
)
```

Race, Ethnicity, and Homelessness {data-navmenu="Explore"}
=========================================

Column
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### Race and Ethnicity

```{r race-plots, echo=F}
black_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
ggplot(mapping = aes(x = percent_nhblack, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Pct Black",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

hispanic_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>%
ggplot(mapping = aes(x = percent_hispanic, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Pct Hispanic",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

just_vulnerability_map <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
  ggplot(aes(fill = Vulnerability)) +
    geom_sf_interactive(
    aes(
      tooltip = Name.x,
      data_id = GEOID
    ),
    size = 0.2, color = "black"
  ) +
  theme_void() +
  scale_fill_distiller(palette = 'BuGn', direction = 1)

# scale_fill_viridis_c() 

# eviction_map

# rent_burden_homelessness_scatter

# scatterplots <- plot_grid(eviction_homelessness_scatter, rent_burden_homelessness_scatter, ncol = 1, align = "v",
#                           rel_heights = c(1,1),
#   rel_widths = c(1,1))
# 
# scatterplots

# patch_plot <- ((eviction_homelessness_scatter / rent_burden_homelessness_scatter) | eviction_map)  + plot_layout(widths = c(1,1.2))
# 
# layout <- c(
#   area(1, 1),
#   area(2, 2),
#   area(1, 3)
# )
# 
# # plot(layout)
# 
# test_patch <- (eviction_homelessness_scatter + plot_spacer() + rent_burden_homelessness_scatter) / (plot_spacer() + eviction_map + plot_spacer()) + plot_layout(heights = c(.75,1))
# 
# test_patch_v2 <- eviction_homelessness_scatter + eviction_map + rent_burden_homelessness_scatter + plot_layout(design = layout)

patch_race_plots <- black_homelessness_scatter + just_vulnerability_map + hispanic_homelessness_scatter

# test_patch

# patch_plot

# rent_burden_homelessness_scatter

# eviction_homelessness_scatter

# test_patch_v2

# eviction_map

girafe(
  ggobj = patch_race_plots,
  height_svg = 5,
  width_svg = 12,
  options = list(
    opts_sizing(rescale = TRUE),
    opts_zoom(max = 5),
    opts_tooltip(css = tooltip_css, delay_mouseover = 0, delay_mouseout = 0),
    opts_hover(css = "fill: #E69F00; fill-opacity: 1.0; stroke: #E69F00;")
  )
)
```

Social Support and Homelessness {data-navmenu="Explore"}
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Column
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### Social Service and Civic Organizations

```{r social-plots, echo=F}
social_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10,
         social_service_orgs_per1k < 10) %>% 
ggplot(mapping = aes(x = social_service_orgs_per1k, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Social Service Orgs Per 1000 People",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

# social_homelessness_scatter

civic_homelessness_scatter <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10, 
         civic_orgs_per1k < 5) %>%
ggplot(mapping = aes(x = civic_orgs_per1k, y = counts_per_capita)) +
  geom_point_interactive(
    aes(tooltip = Name.x,
        data_id = GEOID),
    na.rm = TRUE, size = 1.25,
    color = "#08519c", fill = "#0072B280"
  ) + 
  geom_smooth(method = 'lm', se = F, linetype = 2, size = .5, color = 'black') + 
  labs(x = "Civic Orgs Per 1000 People",
       y = "Homelessness Rate") +
  theme_minimal_grid() +
  theme(axis.text = element_text(size = 6),
        axis.title = element_text(size = 7))

# civic_homelessness_scatter

just_vulnerability_map <- tracts_sf_index_orig_data %>% 
  filter(filing_rate_5yr < 10) %>% 
  ggplot(aes(fill = Vulnerability)) +
    geom_sf_interactive(
    aes(
      tooltip = Name.x,
      data_id = GEOID
    ),
    size = 0.2, color = "black"
  ) +
  theme_void() +
  scale_fill_distiller(palette = 'BuGn', direction = 1)

# scale_fill_viridis_c() 

# eviction_map

# rent_burden_homelessness_scatter

# scatterplots <- plot_grid(eviction_homelessness_scatter, rent_burden_homelessness_scatter, ncol = 1, align = "v",
#                           rel_heights = c(1,1),
#   rel_widths = c(1,1))
# 
# scatterplots

# patch_plot <- ((eviction_homelessness_scatter / rent_burden_homelessness_scatter) | eviction_map)  + plot_layout(widths = c(1,1.2))
# 
# layout <- c(
#   area(1, 1),
#   area(2, 2),
#   area(1, 3)
# )
# 
# # plot(layout)
# 
# test_patch <- (eviction_homelessness_scatter + plot_spacer() + rent_burden_homelessness_scatter) / (plot_spacer() + eviction_map + plot_spacer()) + plot_layout(heights = c(.75,1))
# 
# test_patch_v2 <- eviction_homelessness_scatter + eviction_map + rent_burden_homelessness_scatter + plot_layout(design = layout)

patch_social_plots <- social_homelessness_scatter + just_vulnerability_map + civic_homelessness_scatter

# test_patch

# patch_plot

# rent_burden_homelessness_scatter

# eviction_homelessness_scatter

# test_patch_v2

# eviction_map

girafe(
  ggobj = patch_social_plots,
  height_svg = 5,
  width_svg = 12,
  options = list(
    opts_sizing(rescale = TRUE),
    opts_zoom(max = 5),
    opts_tooltip(css = tooltip_css, delay_mouseover = 0, delay_mouseout = 0),
    opts_hover(css = "fill: #E69F00; fill-opacity: 1.0; stroke: #E69F00;")
  )
)
```

About {data-navmenu="Explore"}
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About the index:

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Data sources: